Customer Stories

98 results

MongoDB Helps Asian Retailers Scale and Innovate at Speed

More retailers across ASEAN are looking to the document database model to support the expansion of their businesses and respond quickly to ever-more-rapidly changing customer demands. Here are two stories shared during our MongoDB.local events in Indonesia and Malaysia in September 2024. Simplicity and offline availability: EasyEat empowers merchants to optimize dining experiences with MongoDB Atlas EasyEat delivers a software-as-a-service (SaaS) point-of-sale (POS) system tailored for restaurants. It simplifies daily operations, optimizes costs, and enhances customer satisfaction for merchants that provide food delivery and pickup services. The platform launched in 2020, and in less than 4 years it has grown to serve over 1,300 merchants and over four million consumers across Malaysia and Indonesia. Speaking at MongoDB.local Kuala Lumpur in September 2024 , Deepanshu Rawat, Engineering Manager at EasyEat, explained how MongoDB Atlas empowered EasyEat to rapidly scale its operations across both the merchant POS and consumer applications. EasyEat’s move from a SQL database to MongoDB Atlas also delivered greater flexibility, enabling faster product development and ease of use for the engineering team. For EasyEat, MongoDB Atlas is more than just a database. The retailer is making full use of the developer data platform’s unique features, including: Analytics node: EasyEat must regularly provide reports to its merchants. These queries tend to be complex, taking significant time to process and putting an excessive load on the system. “With MongoDB Atlas’s analytics node , we are able to process those heavy queries without it impacting our daily operations,” said Rawat. Atlas Triggers: EasyEat uses this feature to perform a range of asynchronous operations. “Using Atlas Triggers helps us optimize the performance of our applications,” said Rawat. MongoDB Atlas Search: EasyEat has started using MongoDB Atlas Search to execute faster and more efficient searches as its platform’s user base grows. “Atlas Search enables us to make searches in our user application very smooth, and on our end, we don’t face any delay or latency issues,” said Rawat. In addition, EasyEat is exploring a few other capabilities on MongoDB, including online archiving . The company is also considering how it can use generative AI via MongoDB Atlas Vector Search to build a personalized recommendations engine. From 10 seconds to 1: Alfamart drives 1,000% efficiency using MongoDB Atlas Alfamart is a leading retailer with over 19,000 stores across Indonesia and the Philippines. It serves 18.1 million customers and handles approximately 4.6 million retail transactions daily. Speaking at MongoDB.local Jakarta in September 2024 , Alfamart’s Chief Technology Officer, Bambang Setyawan Djojo, shared insights into how the company has used MongoDB Atlas to sustain massive scale and to power its digital transformation. The 2015-2020 period was critical for Alfamart. It was in the midst of rapid expansion and had an ambitious digital transformation agenda. In early 2020, as the COVID-19 pandemic began, Alfamart’s offline transactions plummeted while its online transactions soared. “The growth of online transactions was not linear but exponential,” said Setyawan Djojo. “This was the moment: We knew we needed the tools to adapt quickly and go to market fast. This is when we decided to look for a new database.” With its previous SQL database, Alfamart struggled to handle the growing data load, particularly during peak hours. MongoDB Atlas’s flexible document database model delivered greater efficiency for Alfamart’s team of 350 developers. It also smoothly accommodated Alfamart’s need for sudden and significant upscaling. “Fast processing times are critical to keep our customers happy,” said Setyawan Djojo. “It used to take us 10 seconds to scan members during peak hours, but with MongoDB, it is now below one second.” Setyawan Djojo added, “MongoDB helped us eliminate a lot of downtime compared to our previous SQL database.” MongoDB Atlas’s auto-scaling capabilities were a game changer for Alfamart. “MongoDB can automatically scale up and down depending on the usage of resources and performance. So during peak times, the database can scale up, and once the transaction peak is passed, it can scale back down,” said Setyawan Djojo. Looking ahead, Alfamart plans to continue exploring the potential of the MongoDB Atlas platform to further increase productivity, efficiency, and flexibility. Visit our solutions page to learn more about how MongoDB is helping retailers innovate worldwide. Check out our quick-start guide to get started with MongoDB Atlas Vector Search today. Visit our product page to learn more about MongoDB Atlas Search .

November 12, 2024

Health-Tech Startup Aktivo Labs Scales Up With MongoDB Atlas

Aktivo Labs , a pioneering health-tech startup based in Singapore, has made significant strides in the fight against chronic diseases. Aktivo Labs develops innovative preventative healthcare technology solutions that encourage healthier lifestyles. The Aktivo Score ® —the flagship product of Aktivo Labs built on MongoDB Atlas —is a simple yet powerful tool designed to guide users toward healthier living. “By collecting and analyzing data from smartphones and wearables—including physical activity, sleep patterns, and sedentary behavior—the Aktivo Score provides personalized recommendations to help users improve their health,” said Aktivo Labs CTO Jonnie Avinash at MongoDB.local Singapore in August 2024 . Aktivo Labs also works closely with insurance companies. Acting as a data processor, it helps insurers integrate some of the Aktivo Score features into their own apps to improve customer engagement. Empowering insurers with out-of-the-box apps and user journeys From the start, the Aktivo Labs engineering team chose to work on MongoDB Atlas because the platform’s document model and cloud nature provided the flexibility and scalability required to support the company’s business model. The first goal of the engineering team was to enable insurance providers to integrate Aktivo Score smoothly within their own infrastructures. The team built software development kits (SDKs) that insurers can embed in various iOS and Android apps. The SDKs enable progressive web app journeys for user experience, which insurers can then rebrand and customize as their own. Next, the Aktivo Labs team created a web portal to help companies manage their apps and monitor their performance. This required discreet direct integrations with a myriad of wearables. “When we started to deploy things with companies, we were able to replicate this architecture so we could support all kinds of configurations,” Avinash said. “We could give you dedicated clusters if the number of users that you’re expecting is big enough. If you’re not expecting too many customers, we could give you colocated or shared environments.” Finding more efficiencies, flexibility, and scalability with MongoDB Atlas “When we started off, one of our challenges was that we had a very small engineering team. A lot of the focus had to be on functionality, and the cost of tech had to be kept low,” said Avinash. Working on MongoDB Atlas allowed the Aktivo Labs team to focus on product development rather than on database management and overhead costs. As the company grew and expanded to markets across Asia, Africa, and the Middle East, another challenge arose: Aktivo Labs needed to ensure its platform could scale and handle large volumes of disparate data efficiently. MongoDB Atlas was the optimal solution because its fully managed multi-cloud platform could easily scale as the company grew. MongoDB Atlas also provided Aktivo Labs the flexibility it needed to handle the wide variety, volume, and complexity of data generated by users’ health metrics. Based on insights from the MongoDB Atlas oplog, the engineering team made proactive updates to the database in real-time in anticipation of dynamic changes to leaderboards and challenges in the app. This approach enables Aktivo Labs to manage complex data flows efficiently, ensuring that users always have access to the latest metrics about their health. MongoDB Atlas’s secondary nodes and analytics nodes provide isolated environments for intensive data processing tasks, such as calculating risk scores for diabetes and hypertension. This separation ensures that the primary user-facing applications remain responsive, even during periods of heavy data processing. These isolated environments have also been an important factor in achieving compliance with the data-anonymization requirements from health insurers. “The moment you start showing that it’s a managed service and you’re able to show a lot of these things, the amount of faith that both auditors and clients have in us is a lot more,” said Avinash. Powered by MongoDB Atlas, Aktivo Labs is now looking to expand into U.S. and European markets, pursuing its mission of preventing chronic diseases on a global scale. Visit our product page to learn more about MongoDB Atlas.

October 29, 2024

Gamuda Puts AI in Construction with MongoDB Atlas

Gamuda Berhad is a leading Malaysian engineering and construction company with operations across the world, including in Australia, Taiwan, Singapore, Vietnam, the United Kingdom, and more. The company is known for its innovative approach to construction through the use of cutting-edge technology. Speaking at MongoDB.local Kuala Lumpur in August 2024 , John Lim, Chief Digital Officer at Gamuda said: “In the construction industry, AI is increasingly being used to analyze vast amounts of data, from sensor readings on construction equipment to environmental data that impacts project timelines.” One of Gamuda’s priorities is determining how AI and other tools can impact the company’s methods for building large projects across the world. For that, the Gamuda team needed the right infrastructure, with a database equipped to handle the demands of modern AI-driven applications. MongoDB Atlas fulfilled all the requirements and enabled Gamuda to deliver on its AI-driven goals. Why Gamuda chose MongoDB Atlas “Before MongoDB, we were dealing with a lot of different databases and we were struggling to do even simple things such as full-text search,” said Lim. “How can we have a tool that's developer-friendly, helps us scale across the world, and at the same time helps us to build really cool AI use cases, where we're not thinking about the infrastructure or worrying too much about how things work but are able to just focus on the use case?” After some initial conversations with MongoDB, Lim’s team saw that MongoDB Atlas could help it streamline its technology stack, which was becoming very complex and time consuming to manage. MongoDB Atlas provided the optimal balance between ease of use and powerful functionality, enabling the company to focus on innovation rather than database administration. “I think the advantage that we see is really the speed to market. We are able to build something quickly. We are fast to meet the requirements to push something out,” said Lim. Chi Keen Tan, Senior Software Engineer at Gamuda, added, “The team was able to use a lot of developer tools like MongoDB Compass , and we were quite amazed by what we can do. This [ability to search the items within the database easily] is just something that’s missing from other technologies.” Being able to operate MongoDB on Google Cloud was also a key selling point for Gamuda: “We were able to start on MongoDB without any friction of having to deal with a lot of contractual problems and billing and setting all of that up,” said Lim. How MongoDB is powering more AI use cases Gamuda uses MongoDB Atlas and functionalities such as Atlas Search and Vector Search to bring a number of AI use cases to life. This includes work implemented on Gamuda’s Bot Unify platform, which Gamuda built in-house using MongoDB Atlas as the database. By using documents stored in SharePoint and other systems, this platform helps users write tenders quicker, find out about employee benefits more easily, or discover ways to improve design briefs. “It’s quite incredible. We have about 87 different bots now that people across the company have developed,” Lim said. Additionally, the team has developed Gamuda Digital Operating System (GDOS), which can optimize various aspects of construction, such as predictive maintenance, resource allocation, and quality control. MongoDB’s ability to handle large volumes of data in real-time is crucial for these applications, enabling Gamuda to make data-driven decisions that improve efficiency and reduce costs. Specifically, MongoDB Atlas Vector Search enables Gamuda’s AI models to quickly and accurately retrieve relevant data, improving the speed and accuracy of decision-making. It also helps the Gamuda team find patterns and correlations in the data that might otherwise go unnoticed. Gamuda’s journey with MongoDB Atlas is just beginning as the company continues to explore new ways to integrate technology into its operations and expand to other markets. To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start page.

October 22, 2024

Grab Drives 50% Efficiencies with MongoDB Atlas

Grab is Southeast Asia’s leading ‘super application,’ offering a wide range of services, targeting both consumers and businesses, including deliveries, mobility, financial services, enterprise and more. Their range of applications, such as the popular Grab Taxi, Grab Pay, Grab Mart, Grab Ads, and more, count approximately 38 million active users monthly across 500 cities and eight countries. Managing a high volume of constantly growing users and handling regular spikes in demand and activity means that Grab needs to maintain a robust, scalable, and flexible digital infrastructure. Presenting at MongoDB.local Singapore in 2024, Grab shared their journey of migrating one of their key service apps— GrabKios —from the Community Edition of MongoDB to MongoDB Atlas . Grab also described how they are expanding their use of MongoDB to support semantic search. “Transitioning to MongoDB Atlas was not just a migration—it was a strategic move aimed at enhancing our database infrastructure,” said Jude Dulaj Lakshan De Croos, Database Engineering Manager at Grab. A smooth transition to MongoDB Atlas Grab’s journey with MongoDB Atlas began with the realization that their existing database infrastructure, while functional, was not equipped to handle the scale and complexity of their operations. Grab’s eventual migration to MongoDB Atlas was meticulously planned and executed, including extensive testing to ensure a smooth transition. During the critical testing phase, the creation of a replica “prod clone” environment, allowed Grab to test and refine their migration strategy. This minimized the possibility of unforeseen issues. The migration also involved the use of Mongomirror . This facilitated the seamless transfer of data from Grab’s self-hosted clusters to MongoDB Atlas. “We were able to ensure that migration was actually smooth and ran without any issues,” said Swarit Arora, Senior Database Engineer at Grab. MongoDB Atlas’s developer data platform offers Grab high levels of flexibility and scalability, accommodating Grab’s fast growth (the company recorded a 23% revenue growth YoY in 2024) in an ever-changing digital landscape. MongoDB Atlas also delivers unique automation and streamlining capabilities, as well as enterprise-grade support which led to improved process and database management efficiency. Efficiency gains with greater scalability, flexibility, performance MongoDB Atlas provided Grab with an automated, scalable, and secure platform, which empowered its engineering teams to focus on product development rather than database maintenance. “With MongoDB Atlas, we don’t have to worry about the scaling changes. And with hands-on security we can deliver secure and fast applications,” said Arora. “Being able to configure the exact resources required and then scale up and down based on our requirements is a plus. Considering we don't have to manage the scalability part, this is, I think, saving us around 50% of the time.” Furthermore, MongoDB Atlas delivers proactive recommendations to Grab’s team. For example, MongoDB Atlas’s Performance Advisor saves the team time by delivering real-time insights and recommendations to optimize query performance, ultimately reducing manual management tasks and increasing database efficiency. “It is now easy to set up our MongoDB clusters compared to what we were doing when we self-hosted, which was more time-consuming,” added Arora. “Secondly, if we are required to upgrade the cluster version, it is as easy as the click of a button.” Dedicated analytics nodes mean that Grab’s team is able to enhance the analytical capabilities of any application running on MongoDB. The successful migration to MongoDB Atlas has positioned Grab to explore new possibilities, including leveraging MongoDB’s advanced features for use cases such as semantic search and AI applications. Learn more about MongoDB Atlas .

October 14, 2024

magicpin Builds India's Largest Hyperlocal Retail Platform on MongoDB

Despite its trillion-dollar economy, 90% of retail consumption in India still takes place offline . While online retail in India has grown in recent years, much of it still consists of dark stores (a retail outlet or distribution center that exists exclusively for online shopping) and warehouses, the majority of retail establishments—fashion, food, dining, nightlife, and groceries—thrive as physical stores. What’s more, businesses looking to transition to online models are hindered by major platforms that focus primarily on clicks rather than encouraging transactions. This opportunity was the inspiration for the founders of magicpin , India’s largest hyperlocal retail platform. magicpin has revolutionized the conventional pay-per-click model, where businesses bid on keywords or phrases related to their products or services and then pay a fee each time someone clicks on an ad, with a new pay-per-conversion strategy. In a pay-per-conversion model, businesses only pay when they make an actual sale of a product or item. magicpin does not rely on dark stores, warehouses, or deep discounting; instead, it collaborates with local retailers, augmenting foot traffic and preserving the essence of local economies. This unique model ensures that consumers not only enjoy existing in-store benefits, but also receive additional perks when opting to transact through magicpin. “We enable the discovery of those merchants,” says Kunal Gupta, senior vice president at magicpin. “Which merchants in your local neighborhood are selling interesting stuff? What’s their inventory? What savings can we offer to buyers? We have data for everything.” Effectively three SaaS platforms in one, magicpin is a seller app, a buyer app, and a developing logistics app on the Open Network for Digital Commerce ( ONDC ), which is backed by the Indian government. With over 10 million users on its platform (covering the majority of Indian cities and over 100 localities), magicpin has established itself as a leading offline retail discovery and savings app. magicpin currently has 250,000 merchants in categories ranging from food to fashion to pharmacy. The power behind magicpin has always been MongoDB's flexibility and scalability. And from the company’s start in 2015, it became clear that magicpin was on to something special. “In the first week of March 2023 when we onboarded ONDC, we hit almost 10,000 transactions a day. In October last year, we peaked at 50,000 orders in a single day, which is a huge milestone,” says Kunal. “When an ONDC order is placed, it flows through us. We manage the entire process—from sending the order to the merchant, assigning logistics personnel for pickup and delivery, to handling any customer support tickets that may arise. It's the seamless integration of these elements that defines our contribution to the intricate framework of ONDC." Having launched using the community version of MongoDB , Kunal realized that magicpin needed to make better use of its relatively lean tech team and allow them to focus more on building the business. He also saw that a managed service would be a more effective way of handling maintenance and related tasks. “We realized there had to be a better solution. We can’t afford to have all the database expertise tied up with a team that’s focusing on creating businesses and building applications,” said Kunal. “That’s when we started to use MongoDB Atlas." magicpin uses a multitude of technologies, to store over 600 million SKUs, and handle its SaaS platform, session cache, card, and order management, and MongoDB Atlas sits at the heart of the business. “For our operational and scaling needs, it’s seamless,” Kunal concludes. “Availability is high, and monitoring and routing are super-good. Our lives have become much easier.” Watch the full presentation on YouTube to learn more.

July 23, 2024

Nokia Corteca Scales Wi-Fi Connectivity to Millions of Devices With MongoDB Atlas

Nokia’s home Wi-Fi connectivity cloud platform was launched in 2019 as the Nokia WiFi Cloud Controller (NWCC). In 2023, it was renamed and relaunched as the Corteca Home Controller, becoming part of the Corteca software suite that delivers smarter broadband for a better experience. The Corteca Home Controller can be hosted on Amazon Web Services, Google Cloud, or Microsoft Azure, and is the industry’s first platform to support three management services—device management, Wi-Fi management, and application management. Supporting TR-369 (a standardized remote device management protocol) also allows the Home Controller to work in a multi-vendor environment, managing both Nokia broadband devices and third-party broadband devices. By solving connectivity issues before the end-user detects them, and by automatically optimizing Wi-Fi performance, the Home Controller helps deliver excellent customer experiences to millions of users, 24/7. During the five years that Nokia Corteca has been a MongoDB Atlas customer, the Home Controller has successfully scaled from 500,000 devices to over 4.5 million. There are now 75 telecommunications customers of Home Controller spread across all regions of the globe. Having the stability, efficiency, and performance to scale Nokia Corteca's solution is end-to-end, from applications embedded in the device, through the home, and into the cloud. Algorithms assess data extracted from home networks, based on which performance parameters automatically adjust as needed—changing Wi-Fi channels to avoid network interference, for example—thereby ensuring zero downtime. The Home Controller processes real-time data sent from millions of devices, generating massive volumes of data. With a cloud optimization team tasked with deploying the solution across the globe to ever more customers, the Home Controller needed to store and manage its vast dataset and to onboard new telecommunication organizations more easily without incurring any downtime. Prior to Nokia Corteca moving to MongoDB Atlas, its legacy relational database lacked stability and required both admin and application teams to manage operations. A flexible model with time series capabilities That's where MongoDB Atlas came in. Nokia was familiar with the MongoDB Atlas database platform, having already worked with it as part of a previous company acquisition and solution integration. As Nokia's development team had direct experience with the scalability, manageability, and ease of use offered by MongoDB Atlas, they knew it had the potential to address the Home Controller’s technical and business requirements. There was another key element: Nokia wanted to store time-series data—a sequence of data points in which insights are gained by analyzing changes over time. MongoDB Atlas has the unique ability to store operational and time series data in parallel and provides robust querying capabilities on that data. Other advantages include MongoDB's flexible schema, which helps developers store data to match the application's needs and adapt as data changes over time. MongoDB Atlas also provides features such as Performance Advisor that monitors the performance of the database and makes intelligent recommendations to optimize and improve the performance and resource consumption Fast real time data browsing and scalability made easy Previously, scaling the database had been time-consuming and manual. With MongoDB Atlas, the team can easily scale up as demand increases with very little effort and no downtime. This also means it is much more straightforward to add new clients, such as large telecommunications companies. Having started with 100GB of data, the team now has more than 1.3 terabytes, and can increase the disc space in a fraction of a second, positioning the team to be able to scale with the business. As the Home Controller grows and onboards more telcos, the team anticipates a strengthening relationship with MongoDB. “We have a very good relationship with the MongoDB team,” said Jaisankar Gunasekaran, Head of Cloud Hosting and Operations at Nokia. “One of the main advantages is their local presence—they’re accessible, they’re friendly, and they’re experts. It makes our lives easier and lets us concentrate on our products and solutions.” To learn more about how MongoDB can help drive innovation and capture customer imaginations, check out our MongoDB for Telecommunications page.

July 2, 2024